LSTM:将C3D Net得到的结果输入到LSTM中,使得特征从clip级别扩展到数秒级。 MDN & GMM:最后使用mixture density networks获取Gaussian Mixture Model的参数,最后由Gaussian Mixture Model得到显著图上每个像素显著性的概率分布。最后通过此分布可以重新对视频进行加权,完成我们的visual attention model。 训练: 本文在GMM后...
First, we elaborate a recurrent mixture density network for explicit modeling of the time conditional mixing coefficients, as well as the means and variances of its Gaussian mixture components. Second, we derive training equations with which all the network weights are inferred in the maximum ...
[1] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157–166, March 1994. [2] C. Bishop. Mixture density networks. Technical report, 1994. [3] C. Bishop. Neural Networks for Pattern ...
This architecture, featuring recurrent connections within the weight, mean, and variance neural networks, adeptly captures demand trends, thus significantly enhancing forecasting precision, particularly in high-volatility scenarios. Our comprehensive experimental analysis, utilizing real-world MoD datasets, ...
Generating Sequences With Recurrent Neural Networks
Xu et al. [27] develop LSTM networks alongside Mixture Density Networks (MDNs) for predicting taxi demand in New York City (USA) zones using previous demand data and other information (weather, time, taxi drop-offs, etc.). Show abstract Traffic prediction using artificial intelligence: Review ...
Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information p
We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices. In this work, we...
A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate inject...
High speed side-view videos of sliding drops enable researchers to investigate drop dynamics and surface properties. However, understanding the physics of sliding requires knowledge of the drop width. A front-view perspective of the drop is necessary. In